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February 24, 2020 @ 4:00 pm - 5:00 pm
Title: Modern Perspectives on Classical Learning Problems: Role of Memory and Data Amplification
Presenter: Vastal Sharan PhD Student Stanford University
Abstract: This talk will discuss statistical and computation requirements—and how they interact—for three learning setups. In the first part, we inspect the role of memory in learning. We study how the total memory available to a learning algorithm affects the amount of data needed for learning (or optimization), beginning by considering the fundamental problem of linear regression. Next, we examine the role of long-term memory vs. short-term memory for the task of predicting the next observation in a sequence given the past observations. Finally, we explore the statistical requirements for the task of manufacturing more data—namely how to generate a larger set of samples from an unknown distribution. Can “amplifying” a dataset be easier than learning?